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An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

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Published on: February 12, 2018

A method for online estimation of human arm dynamics.

Farid Mobasser¹, Keyvan Hashtrudi-Zaad

¹Dept. of Electr. & Comput. Eng., Queen's Univ., Kingston, ON, Canada.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|October 20, 2007

Summary

This study presents a new method for estimating human forearm dynamics in real-time. It uses a novel approach combining local parameter identification with artificial neural networks for accurate human body performance analysis.

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Area of Science:

Biomechanics
Robotics
Machine Learning

Background:

Human arm dynamics are crucial for performance analysis and human-machine interfaces.
Accurate real-time estimation of these dynamics remains a challenge.

Purpose of the Study:

To introduce a novel method for online estimation of human forearm dynamics.
To develop a model applicable to human body performance analysis and control of human-machine interfaces.

Main Methods:

Utilized a second-order quasi-linear model for forearm dynamics.
Employed Moving Window Least Squares for local parameter identification.
Trained a Radial Basis Function Artificial Neural Network for interpolation/extrapolation of dynamic parameters.

Main Results:

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Successfully estimated human forearm dynamic parameters online.
Demonstrated the method's efficacy on a single degree-of-freedom robotic arm.
Validated the model's ability to interpolate/extrapolate parameters across variable operating points.

Conclusions:

The proposed method offers an effective approach for real-time human forearm dynamic estimation.
This technique has potential applications in advanced human-machine interfaces and biomechanical analysis.
The integration of local identification and neural networks provides a robust estimation framework.